During recent years, the accelerated evolution of online fashion trends and seasonal dynamics has increased the demand for intelligent recommendation systems capable of balancing personalization and novelty. This study presents a hybrid artificial-intelligence-based fashion recommender system that generates personalized and trend-aware clothing suggestions based on the contents of users’ virtual closets. The proposed model integrates item-based collaborative filtering (IBCF) and content-based filting (CBF), augmented by a Sugeno fuzzy-logic module that computes a continuous trend percentage (T) using normalized sales, likes, and views. Experiments were conducted on a dataset of 4,224 items using simulated virtual closet ranging from 40 to 80 items. Performance was evaluated using precision at rank 10 (P@10), recall at rank 10 (R@10), and Normalized Discounted Cumulative Gain at rank 10 (NDCG@10), in addition to runtime scalability vs . catalog size. The proposed fuzzy-hybrid model consistently outperformed pure content-based, pure collaborative, and non-fuzzy hybrid baselines in recommendation accuracy while maintaining linear scalability. The results indicate that fuzzy-logic-based trend modeling effectively enhances the balance between stylistic consistency and exposure to emerging fashion trends within the evaluated experimental setting. However, the observed improvements should be interpreted cautiously because the experiments rely on simulated user closets and a private dataset. The findings suggest that the proposed approach can inform the design of adaptive fashion recommendation systems. However, results are obtained under a controlled simulation setting with manually designed fuzzy membership functions, and real-user validation remains an avenue for future work.
Masri et al. (Mon,) studied this question.
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